{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GI37U9A7xugc"
   },
   "source": [
    "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n",
    "<div class=\"align-center\">\n",
    "<a href=\"https://unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
    "<a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n",
    "<a href=\"https://docs.unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a></a> Join Discord if you need help + \u2b50 <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> \u2b50\n",
    "</div>\n",
    "\n",
    "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).\n",
    "\n",
    "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gtoVcEVQxugg"
   },
   "source": [
    "### News"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YZeyOoO_xugh"
   },
   "source": [
    "\n",
    "Introducing FP8 precision training for faster RL inference. [Read Blog](https://docs.unsloth.ai/new/fp8-reinforcement-learning).\n",
    "\n",
    "Unsloth's [Docker image](https://hub.docker.com/r/unsloth/unsloth) is here! Start training with no setup & environment issues. [Read our Guide](https://docs.unsloth.ai/new/how-to-train-llms-with-unsloth-and-docker).\n",
    "\n",
    "[gpt-oss RL](https://docs.unsloth.ai/new/gpt-oss-reinforcement-learning) is now supported with the fastest inference & lowest VRAM. Try our [new notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-GRPO.ipynb) which creates kernels!\n",
    "\n",
    "Introducing [Vision](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl) and [Standby](https://docs.unsloth.ai/basics/memory-efficient-rl) for RL! Train Qwen, Gemma etc. VLMs with GSPO - even faster with less VRAM.\n",
    "\n",
    "Visit our docs for all our [model uploads](https://docs.unsloth.ai/get-started/all-our-models) and [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_lX9gGbhxugj"
   },
   "source": [
    "### Installation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "CoVJPS_uUghm"
   },
   "outputs": [],
   "source": "%%capture\nimport os, importlib.util\n!pip install --upgrade -qqq uv\nif importlib.util.find_spec(\"torch\") is None or \"COLAB_\" in \"\".join(os.environ.keys()):    \n    try: import numpy, PIL; get_numpy = f\"numpy=={numpy.__version__}\"; get_pil = f\"pillow=={PIL.__version__}\"\n    except: get_numpy = \"numpy\"; get_pil = \"pillow\"\n    !uv pip install -qqq \\\n        \"torch>=2.8.0\" \"triton>=3.4.0\" {get_numpy} {get_pil} torchvision bitsandbytes \"transformers==4.56.2\" \\\n        \"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo\" \\\n        \"unsloth[base] @ git+https://github.com/unslothai/unsloth\" \\\n        git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\nelif importlib.util.find_spec(\"unsloth\") is None:\n    !uv pip install -qqq unsloth\n!uv pip install --upgrade --no-deps transformers==4.56.2 tokenizers trl==0.22.2 unsloth unsloth_zoo"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xRwOqyyOUghp"
   },
   "source": [
    "### Unsloth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r2v_X2fA0Df5"
   },
   "source": [
    "We're about to demonstrate the power of the new OpenAI GPT-OSS 20B model through an inference example. For our `bnb-4bit` version, use this [notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/GPT_OSS_BNB_(20B)-Inference.ipynb) instead.\n",
    "\n",
    "**We're using OpenAI's MXFP4 Triton kernels combined with Unsloth's kernels!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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    },
    "id": "QmUBVEnvCDJv",
    "outputId": "782a10dc-67c4-4580-913c-1ad7b17f7d64"
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   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\ud83e\udda5 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
      "\ud83e\udda5 Unsloth Zoo will now patch everything to make training faster!\n",
      "==((====))==  Unsloth 2025.8.5: Fast Gpt_Oss patching. Transformers: 4.56.0.dev0.\n",
      "   \\\\   /|    Tesla T4. Num GPUs = 1. Max memory: 14.741 GB. Platform: Linux.\n",
      "O^O/ \\_/ \\    Torch: 2.8.0+cu128. CUDA: 7.5. CUDA Toolkit: 12.8. Triton: 3.4.0\n",
      "\\        /    Bfloat16 = FALSE. FA [Xformers = None. FA2 = False]\n",
      " \"-____-\"     Free license: http://github.com/unslothai/unsloth\n",
      "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n",
      "Unsloth: Using float16 precision for gpt_oss won't work! Using float32.\n",
      "Unsloth: QLoRA and full finetuning all not selected. Switching to 16bit LoRA.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "model.safetensors.index.json: 0.00B [00:00, ?B/s]"
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     "metadata": {}
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    {
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       "model-00000-of-00002.safetensors:   0%|          | 0.00/4.79G [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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       "model_id": "6df910aa938f4895aae5731354986163"
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     "metadata": {}
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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       "model_id": "93ed61c198304cbcae6c82bfe3a6e544"
      }
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     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
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       "model-00002-of-00002.safetensors:   0%|          | 0.00/4.17G [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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       "model_id": "58089eb31f74404b81a9389c7f8b5499"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
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      }
     },
     "metadata": {}
    },
    {
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "d75c857f71144130b7fcb73f81a34add"
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     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "tokenizer_config.json: 0.00B [00:00, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "d04c04394bda4453a82d2fa287377754"
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     },
     "metadata": {}
    },
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      ],
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     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "9b053f12cd7243bdb39db8994dafd1e4"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "chat_template.jinja: 0.00B [00:00, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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       "model_id": "c4dcdacf7ce34f5e9c471a35984e9181"
      }
     },
     "metadata": {}
    }
   ],
   "source": [
    "from unsloth import FastLanguageModel\n",
    "import torch\n",
    "\n",
    "# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n",
    "fourbit_models = [\n",
    "    \"unsloth/gpt-oss-20b-unsloth-bnb-4bit\", # 20B model using bitsandbytes 4bit quantization\n",
    "    \"unsloth/gpt-oss-120b-unsloth-bnb-4bit\",\n",
    "    \"unsloth/gpt-oss-20b\", # 20B model using MXFP4 format\n",
    "    \"unsloth/gpt-oss-120b\",\n",
    "] # More models at https://huggingface.co/unsloth\n",
    "\n",
    "model, tokenizer = FastLanguageModel.from_pretrained(\n",
    "    model_name = \"unsloth/gpt-oss-20b\",\n",
    "    dtype = None, # None for auto detection\n",
    "    max_seq_length = 4096, # Choose any for long context!\n",
    "    load_in_4bit = False,  # 4 bit quantization to reduce memory\n",
    "    full_finetuning = False, # [NEW!] We have full finetuning now!\n",
    "    # token = \"hf_...\", # use one if using gated models\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ycmbmR1nUghu"
   },
   "source": [
    "### Reasoning Effort\n",
    "The `gpt-oss` models from OpenAI include a feature that allows users to adjust the model's \"reasoning effort.\" This gives you control over the trade-off between the model's performance and its response speed (latency) which by the amount of token the model will use to think.\n",
    "\n",
    "----\n",
    "\n",
    "The `gpt-oss` models offer three distinct levels of reasoning effort you can choose from:\n",
    "\n",
    "* **Low**: Optimized for tasks that need very fast responses and don't require complex, multi-step reasoning.\n",
    "* **Medium**: A balance between performance and speed.\n",
    "* **High**: Provides the strongest reasoning performance for tasks that require it, though this results in higher latency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DpCQ9QNLUghv",
    "outputId": "3584c793-5f88-4493-88d1-67208e5ac8e6"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\n",
      "Knowledge cutoff: 2024-06\n",
      "Current date: 2025-08-13\n",
      "\n",
      "Reasoning: low\n",
      "\n",
      "# Valid channels: analysis, commentary, final. Channel must be included for every message.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>Equation: x^5 +3x^4 -10 =3 => x^5 +3x^4 -13 =0. Need roots. Could factor numeric? Try integer roots: \u00b11, \u00b113. f(1)=1+3-13=-9. f( -1)= -1+3-13=-1? wait: f(-1)=(-1)+3-13=-12. f(13)=371293+3*28561-13 huge positive. f(-13)= -371293+3*28561-13 = -371293+85683-13= -285724. Maybe rational \u00b113? none. So only real root? Let's find approximate. Use numerical. Let g(x)=x^5+3x^4-13. At x=1: -9. At x=2:32+ 3*16=48 =>80-13=67 positive. So one root between1 and2. Try1.2: 1.2^5= (1.2^2=1.44, *1.2^3? 1.2^3=1.728, *1.2^5 =1.728*1.44= asym? Let's compute: 1.2^4=2.0736, times 1.2 =2.494. So x^5=2.49. 3x^4=3*2.0736=6.2208 sum=8.71-13=-4.29. So still negative. Try1.4: 1.4^2=1.96, ^3=2.752, ^4=384? let's compute: 1.4^4= 1.96*1.96=768? Wait 1.96*1.96=767? compute: 1.96*1.96= 768? Actually 1.96*1.96= 1.926? let me do precisely: 2*2=4 minus small. ok quick approximate: 1.4^4= (1.96)^2=768? 1.96^2= 767.? 1.96^2= 1.96*1.96= 610? I'm messing. Let's use calculator mentally: 1.96*1.96 = (2-0.04)^2=4-0\n"
     ]
    }
   ],
   "source": [
    "from transformers import TextStreamer\n",
    "\n",
    "messages = [\n",
    "    {\"role\": \"user\", \"content\": \"Solve x^5 + 3x^4 - 10 = 3.\"},\n",
    "]\n",
    "inputs = tokenizer.apply_chat_template(\n",
    "    messages,\n",
    "    add_generation_prompt = True,\n",
    "    return_tensors = \"pt\",\n",
    "    return_dict = True,\n",
    "    reasoning_effort = \"low\", # **NEW!** Set reasoning effort to low, medium or high\n",
    ").to(\"cuda\")\n",
    "\n",
    "_ = model.generate(**inputs, max_new_tokens = 512, streamer = TextStreamer(tokenizer))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dHRzt8oKUghx"
   },
   "source": [
    "Changing the `reasoning_effort` to `medium` will make the model think longer. We have to increase the `max_new_tokens` to occupy the amount of the generated tokens but it will give better and more correct answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YTbjWPfdUghx",
    "outputId": "336ca740-f4dc-4b46-9226-131c7637e50a"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\n",
      "Knowledge cutoff: 2024-06\n",
      "Current date: 2025-08-13\n",
      "\n",
      "Reasoning: medium\n",
      "\n",
      "# Valid channels: analysis, commentary, final. Channel must be included for every message.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>We need to solve equation: x^5 + 3x^4 - 10 = 3. So rewrite: x^5 + 3x^4 - 10 = 3 => x^5 + 3x^4 - 13 = 0. So solve polynomial equation x^5 + 3x^4 - 13 = 0. We likely seek real solutions. It's quintic, so analytic roots may be messy. But maybe there's an integer root? We can test integer divisors of 13: \u00b11,\u00b113. Plug x=1: 1+3-13=-9 not zero. x= -1: -1+3-13=-10. x=13 huge. x = -13: -371293 + 121663 -13 huge negative. So no integer root. Maybe rational? \u00b113 maybe not. So probably we can factor something? Let\u2019s try substitution x^4(x+3)=13? Actually equation: x^5 + 3x^4 = 13 so x^4(x+3)=13 => x+3 = 13/x^4 => x = 13/x^4 - 3. Hard.\n",
      "\n",
      "Maybe we find approximate numeric solution? Let's analyze function f(x)=x^5+3x^4-13. Evaluate some values.\n",
      "\n",
      "f(0)=-13. f(1)= -9. f(2)=32 + 48 -13 = 40. So there's root between 1 and 2. f(1.2): 1.2^5= (1.2^2=1.44, 1.2^3=1.728, 1.2^4=2.0736, 1.2^5=2.494). 3*1.2^4=3*2.0736=6.2208. Sum=8.7148-13=-4.285. f(1.3): 1.3^5: 1.3^2=1.69, 1.3^3=2.1957, 1.3^4=2.85681, 1.3^5=587... let's compute 2.85681*1.3= anf: 2.876? Wait; 2.85681*1.3=738? Let's compute precisely: 2.85681*1.3 = 185? Actually 2.85681*1.3 = 39? Wait 2.85681*1=2.85681, times 0.3=0.857043, sum=3.713853. So 1.3^5=3.713853. 3*1.3^4 =3*2.85681=8.57043. Sum=12.38428-13=-0.6157. f(1.35): compute 1.35^2=1.8225, ^3=1.310..., 1.8225*1.35=779? No: 1.8225*1.35=552? Let's calculate: 1.8225*1.35 = 1.8225*1 + 1.8225*0.35 = 1.8225 + 717. So 1.8225*0.35=851? Actually 1.8225*0.35=758? Let's do precisely: 1.8225*35=758.4; divided by 100 =7.584. Wait mistake. Let's use high precision: 1.8225 * 0.35 = 1.602375. Add 1.8225 gives 3.424875. So 1.35^3=3.424875. ^4 multiply by 1.35: 3.424875*1.35 = 3.424875 + 1.01799375 =4.44286875. ^5 multiply by 1.35: 4.44286875*1.35 = 4.44286875 +1.3335860625 =5.7764 approx. So 1.35^5 =5.7764. 3*1.35^4=3*4.442869=13. anon: 13. 3*4.442869=13. pre? 4.442869*3=13. S? Actually 4.442869*3 = 13. ? Let's compute: 4.442869 *3 = 13.? 4.442869*2=8.885738, +4.442869=13.328607. So f(1.35)=5.7764+13.3286-13=+? Sum=5.7764+\n"
     ]
    }
   ],
   "source": [
    "from transformers import TextStreamer\n",
    "\n",
    "messages = [\n",
    "    {\"role\": \"user\", \"content\": \"Solve x^5 + 3x^4 - 10 = 3.\"},\n",
    "]\n",
    "inputs = tokenizer.apply_chat_template(\n",
    "    messages,\n",
    "    add_generation_prompt = True,\n",
    "    return_tensors = \"pt\",\n",
    "    return_dict = True,\n",
    "    reasoning_effort = \"medium\", # **NEW!** Set reasoning effort to low, medium or high\n",
    ").to(\"cuda\")\n",
    "\n",
    "_ = model.generate(**inputs, max_new_tokens = 1024, streamer = TextStreamer(tokenizer))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BpLOtu6dUghy"
   },
   "source": [
    "Lastly we will test it using `reasoning_effort` to `high`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fEuRgVRHUghy",
    "outputId": "9bb48fb6-f2b6-405c-e1bf-b87dd044fdab"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\n",
      "Knowledge cutoff: 2024-06\n",
      "Current date: 2025-08-13\n",
      "\n",
      "Reasoning: high\n",
      "\n",
      "# Valid channels: analysis, commentary, final. Channel must be included for every message.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>The problem: \"Solve x^5 + 3x^4 - 10 = 3.\" It is an equation: x^5 + 3x^4 - 10 = 3. So bring everything: x^5 + 3x^4 - 13 = 0. We need to solve for x? Likely real solutions? Or approximate? Could be factorable? Let's explore algebraically.\n",
      "\n",
      "Given x^5 + 3x^4 - 13 = 0.\n",
      "\n",
      "We can attempt to factor by grouping? Not obviously. Maybe try rational root theorem: possible rational roots are factors of 13: \u00b11, \u00b113. Let's test:\n",
      "\n",
      "x=1: 1 + 3 -13 = -9 \u2260 0\n",
      "x=-1: -1 + 3 -13 = -12 \u2260 0\n",
      "x=13: huge > 0.\n",
      "x=-13: negative large. So no integer roots. Maybe something like x=2? Wait rational roots must divide constant 13, but polynomial is degree 5 with leading coefficient 1. But coefficients: 1,3,0,0,0,-13. So constant term -13, rational roots \u00b11, \u00b113. None work. So no simple rational root.\n",
      "\n",
      "Thus the equation x^5 + 3x^4 - 13 = 0 likely has one real root only? Let's analyze derivative: 5x^4 +12x^3 = x^3 (5x + 12). So derivative zero at x=0 (triply), and at x = -12/5 \u2248 -2.4. So function f(x)=x^5+3x^4-13.\n",
      "\n",
      "We can evaluate f(-3) = (-243) + 81 -13 = -175? Wait compute: (-3)^5 = -243; 3*(-3)^4 = 3*81=243; So f(-3) = -243 + 243 -13 = -13. Actually 243-243=0, so f(-3) = -13. f(-4) = (-1024)+ 192 -13 = -, -1024+192=-, -1024+192 = - (1024-192)= - (1112?) Actually 1024-192=1024-200+8=744+8=752? Wait correct: 1024-192= (1000-192)+(24) is 1108? Let's compute precisely: 1024-192 = 1024-200+8 = 784+8 = 792? No, 1024-200=724, plus 8 = 732. Wait I think I'm messing. Let's do: 192*5=960; 1024-960= 108; plus 192/5=38.4? No. Let's compute more carefully: 1024-192: Subtract 192 from 1024: 1024-100=924; 924-92=832; So 1024-192= 1024-200+8 = 124? Wait no, let's do direct:\n",
      "\n",
      "192*5= 200*5 - 8*5? I'm not computing. Let's compute 1024-192 by decomposition: 1024-200= 722; add 8 back to get 730. Yes, 1024-192= 1024-200+8 = 722+8 = 730. So -1024 + 192 = - (1024-192) = -(730) = -730. Then minus 13 gives -743. So f(-4) = -743, far negative.\n",
      "\n",
      "Now f(-2): (-32) + 48 -13 = -245? Actually -32+48=16; 16-13=3. So f(-2) = 3.\n",
      "\n",
      "f(-1): -1 + 3 -13 = -12. So at x=-2, f(-2)=3, at x=-1, f(-1)=-12. Therefore function crosses zero in between. Since f(-3)=-13, f(-2)=3, so one root between -3 and -2? Wait f(-3) = -13, f(-2)=3. So yes, one root around about -2. something. Also check f(0) = -13, negative. f(1) = 1+3-13=-9 negative. f(2) = 32+48-13=39? Actually 32+48=80; 80-13=67 > 0. Wait but f(1) was -9? Let's compute: x=1: 1^5 =1; 3*1^4 =3; 1+3-13=-9. So yes negative. f(2) = 32+48-13 = 39? 32+48=80, minus 13 =67? Wait 80-13=67. Yes. So sign changes between 1 and 2. But earlier f(0)=-13. f(1) negative. So one root between 1 and 2. But we found f(-2)=3 > 0, f(-1) = -12 negative. That implies a root between -2 and -1. But we also saw f(-3) = -13 negative, f(-2)=3 positive. So another root between -3 and -2. Wait f(-3) negative. So from negative at -3, passes positive at -2, then negative at -1, then negative at 0. So two sign changes in the negative region, meaning potentially two real roots there. But careful: The function is a quintic, so could have up to 5 real roots. But derivative: zeros at x=0 (multiplicity 3) and x=-12/5 \u2248 -2.4. So there is a local max at x=-12/5? Let's analyze f' sign:\n",
      "\n",
      "- For x<-12/5 \u2248 -2.4: factor x^3(5x+12). For x ~ -3: x^3 is negative (-27), 5x+12 at x=-3: 5(-3)+12 = -15+12 = -3, negative. So product negative*negative = positive. So f' > 0 for x < -12/5? Wait: Let's compute: x^3 negative, (5x+12) negative for x < -12/5? x < -2.4, then 5x < -12, +12 negative. So negative. So negative*negative positive. So f' > 0 for x < -12/5, i.e., function increasing until x = -12/5. At x = -12/5, f' = 0. For -12/5 < x < 0: For x say -1: x^3 negative (-1), (5x+12) = -5+12=7 positive. So negative*positive negative => f' < 0, function decreasing. So at x=0, f' = 0 again (multiplicity 3). For x>0, f' > 0 (x^3 positive, 5x+12 positive). So function has a local maximum at x=-12/5, and a stationary plateau at 0 (inflection? Actually triple root of derivative). So global behavior: For large negative x, f(x) \u2192 -\u221e (since leading term x^5 dominates negative). At x = -\u221e, negative. It increases to some value at x = -12/5, then decreasing until x=0, then increasing thereafter. So there can be at most 3 real roots? Let's check sign at f(-\u221e) negative. At f(-12/5), maybe positive? Let's compute f at x = -12/5 exactly: x = -12/5 = -2.4. Compute f(-2.4) = (-2.4)^5 + 3*(-2.4)^4 - 13.\n",
      "\n",
      "Let's compute: (-2.4)^2 = 5.76. (-2.4)^3 = -13.824. (-2.4)^4 = 33.1584. (-2.4)^5 = -79.6269? Actually 33.1584 * -2.4 = -100.600... Wait compute carefully: 33.1584*2.4=79.6269. With negative: -79.6269. So f(-2.4) = (-79.6269) + 3*33.1584 -13. 3*33.1584 = 99.4752. Sum: -79.6269 + 99.4752 = 19.8483. minus 13 = 6.8483. So f(-2.4) \u2248 6.85. So f positive at critical point. At x=0: f(0) = -13 negative. So function crosses zero between -2.4 and 0 at least once (it does: we found root between -2 and -1). But there may be two crossings: positive to negative at -12/5 to 0. Let's note that f is increasing until -12/5 where positive. Then decreasing to a minimum at x=0 where negative. So there should be exactly one root in (-2.4, 0). However earlier we found also that f(-3) negative, f(-2) positive, then f(-1) negative. That would imply two roots between -3 and -2 and -2 and -1. But\n"
     ]
    }
   ],
   "source": [
    "from transformers import TextStreamer\n",
    "\n",
    "messages = [\n",
    "    {\"role\": \"user\", \"content\": \"Solve x^5 + 3x^4 - 10 = 3.\"},\n",
    "]\n",
    "inputs = tokenizer.apply_chat_template(\n",
    "    messages,\n",
    "    add_generation_prompt = True,\n",
    "    return_tensors = \"pt\",\n",
    "    return_dict = True,\n",
    "    reasoning_effort = \"high\", # **NEW!** Set reasoning effort to low, medium or high\n",
    ").to(\"cuda\")\n",
    "\n",
    "_ = model.generate(**inputs, max_new_tokens = 2048, streamer = TextStreamer(tokenizer))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-ANZXGOsxugy"
   },
   "source": [
    "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n",
    "\n",
    "Some other links:\n",
    "1. Train your own reasoning model - Llama GRPO notebook [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb)\n",
    "2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n",
    "3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n",
    "6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n",
    "\n",
    "<div class=\"align-center\">\n",
    "  <a href=\"https://unsloth.ai\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
    "  <a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\" width=\"145\"></a>\n",
    "  <a href=\"https://docs.unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a>\n",
    "\n",
    "  Join Discord if you need help + \u2b50\ufe0f <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> \u2b50\ufe0f\n",
    "</div>\n",
    "\n  This notebook and all Unsloth notebooks are licensed [LGPL-3.0](https://github.com/unslothai/notebooks?tab=LGPL-3.0-1-ov-file#readme).\n"
   ]
  }
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